A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning

Abstract

Optimizing irrigation water usage is crucial for sustainable agriculture, especially in the context of increasing water scarcity and climate variability. Accurate estimation of evapotranspiration (ET), a key component in determining water requirements for crops, is essential for effective irrigation management. Traditional methods of measuring and estimating ET, such as eddy-covariance systems and lysimeters, provide valuable data but often face limitations in scalability, cost, and complexity. Recent advancements in machine learning (ML) offer promising alternatives to enhance the precision and efficiency of ET estimation and smart irrigation systems. This review explores the integration of machine learning techniques in optimizing irrigation water usage, with a particular focus on ET prediction and smart irrigation technologies. We examine various ML models, that have been employed to predict ET using diverse datasets comprising meteorological, soil, and remote sensing data. In addition to ET estimation, the review highlights smart irrigation systems that optimize irrigation schedules based on real-time data inputs. Through this review, we aim to provide a comprehensive overview of the state-of-the-art in ML-based ET estimation and smart irrigation technologies, contributing to the development of more resilient and efficient agricultural water management strategies.